Exploring Cooperative Positioning and Dynamic Base Stations for Potential Vehicular Positioning Accuracy Improvement: A Comprehensive Approach. Guedes, T., Botelho, F., Silva, I., Silva, H., & Pendão, C. In IARIA Congress 2024, The 2024 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications, pages 191-197, 6, 2024.
Exploring Cooperative Positioning and Dynamic Base Stations for Potential Vehicular Positioning Accuracy Improvement: A Comprehensive Approach [link]Website  abstract   bibtex   
Cooperative positioning has appeared as a promising strategy to improve the accuracy of vehicle positioning systems, particularly in urban environments where traditional static base stations are used. This paper proposes an approach for selecting vehicles to serve as dynamic reference stations to improve positioning using Artificial Intelligence, which enables the reduction of costs associated with correction services. By leveraging the presence of nearby vehicles, our method aims to improve the precision of positioning in challenging environments like urban canyons. To achieve this goal, we utilize simulation data to create a comprehensive dataset, capturing various environmental conditions and vehicle dynamics. We then employ machine learning techniques to use this dataset and identify optimal vehicles that can serve as reference stations for improving the positioning accuracy of other vehicles in real-time. By continuously learning and adapting to changing conditions, our approach offers a flexible and robust solution for cooperative positioning in dynamic urban settings.
@inproceedings{
 title = {Exploring Cooperative Positioning and Dynamic Base Stations for Potential Vehicular Positioning Accuracy Improvement: A Comprehensive Approach},
 type = {inproceedings},
 year = {2024},
 pages = {191-197},
 websites = {https://www.thinkmind.org/library/IARIA_CONGRESS/IARIA_Congress_2024/iaria_congress_2024_2_270_50128.html},
 month = {6},
 day = {30},
 id = {e02fca96-bc9c-3eb1-81d8-218de5abd9ab},
 created = {2024-08-11T08:44:06.784Z},
 accessed = {2024-07-05},
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 profile_id = {229eeec8-d35c-3974-bccc-757a0be2a2c4},
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 last_modified = {2024-08-11T08:44:06.784Z},
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 citation_key = {Guedes2024},
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 abstract = {Cooperative positioning has appeared as a promising strategy to improve the accuracy of vehicle positioning systems, particularly in urban environments where traditional static base stations are used. This paper proposes an approach for selecting vehicles to serve as dynamic reference stations to improve positioning using Artificial Intelligence, which enables the reduction of costs associated with correction services. By leveraging the presence of nearby vehicles, our method aims to improve the precision of positioning in challenging environments like urban canyons. To achieve this goal, we utilize simulation data to create a comprehensive dataset, capturing various environmental conditions and vehicle dynamics. We then employ machine learning techniques to use this dataset and identify optimal vehicles that can serve as reference stations for improving the positioning accuracy of other vehicles in real-time. By continuously learning and adapting to changing conditions, our approach offers a flexible and robust solution for cooperative positioning in dynamic urban settings.},
 bibtype = {inproceedings},
 author = {Guedes, Tânia and Botelho, Fabricio and Silva, Ivo and Silva, Helder and Pendão, Cristiano},
 booktitle = { IARIA Congress 2024, The 2024 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications}
}

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